An enormous amount of information has accumulated about the molecular components that govern the cell division cycle and apoptosis, information that is crucial to the development of new anti-cancer therapies. However, the relevant molecular interaction networks are so complicated that it is often difficult to understand how they function. Comprehensive network diagrams, using a standardized notation, therefore are essential in the same way that circuit diagrams are essential in electronics. Diagramming bioregulatory networks however presents a major challenge, because of the large role played by multimolecular complexes and modification contingencies . Several years ago, we devised a notation for molecular interaction maps (MIM) that many in the field now consider to be the best way to represent the networks in detail. We have used MIMs in many publications and examples have been prominently displayed in journal issues (such as being featured on the cover). To generate a MIM, one has to adhere to formal MIM notation rules (described in Kohn et al Mol. Biol. Cell 17:1-13, 2006); this requirement imposes a useful discipline of logic that enforces clear understanding of the available information about a network's structure. The MIM notation has important advantages over other proposed notations and has the unique ability to represent combinatorial complexity in molecular interaction networks (Kohn et al Mol. Systems Biol. doi:10.1038/msb4100088, 2006). In order to make MIMs conveniently accessible, we post them on the internet with links to annotations, references, and to other databases (http://discover.nci.nih.gov/mim/). During the past year, we showed for the first time how MIMs can represent network events at the DNA and chromatin levels. We prepared a MIM describing the network involved in nucleosome disassembly in front of a DNA replication fork, assembly behind the replication fork, and the copying of epigenetic information onto the replicated chromatin. We used this MIM in a comprehensive review of the molecular interactions taking place during these events, thereby showing the advantages of systematizing information in this way (Kohn et al Mol. Biol. Cell 19:1-7, 2008). We have also shown how the MIM notation can depict the events at stalled replication forks (in preparation). In the latter context, we have devised a hierarchical procedure for preparation of orderly MIMs that overcomes the haphazard manner in which network interactions are often portrayed. The p53 tumor suppressor is a key component of the DNA damage response that arrests the progress cells through the cell cycle or causes cells to undergo apoptosis. These responses are governed in large part by the activity of p53 as a transcription factor, which is controlled mainly through interactions with Mdm2 and MdmX. Although the action of Mdm2 on the control of p53 is well understood, the role of Mdmx on this control network is not. We therefore assembled an updated MIM of the interactions that comprise the p53 control network. From that MIM, we extracted for computer simulation an explicit model network that we thought could be the core of the control system involving both Mdm2 and MdmX. In simulations of the model, we surveyed the DNA damage response of p53 activity as a functions of the kinetic parameters controlling Mdm2 and MdmX. We simulated DNA damage as the rate of phosphorylation of p53, Mdm2 and MdmX. The model expressed the consequences of those phosphorylations on the interactions of p53, Mdm2, and Mdmx, and the consequent control of p53 transcriptional activity. The results suggest that MdmX may amplify or stabilize DNA damage-induced p53 responses and that the effects of MdmX are mediated by accumulated reservoirs of p53:MdmX and Mdm2:MdmX heterodimers. A survey of kinetic parameter space disclosed regions of switch-like behavior stemming from such reservoir-based transients. During an early response to DNA damage, MdmX positively or negatively regulated p53 activity, depending on the level of Mdm2, and led to amplification of p53 activity and switch-like response. During a late response to DNA damage, MdmX was observed to dampen oscillations of p53 activity. A possible role of MdmX therefore may be to dampen p53 oscillations that otherwise could produce erratic cell behavior or cell death. We think that metabolic oscillation could make cells vulnerable to catastrophic transients, and that control networks may be designed to dampen such oscillations. Oscillations of p53 could be particularly hazardous, because a transient swing might trigger inappropriate cell death. The ability of MdmX to dampen p53 oscillations in our simulations is therefore of interest. We have now initiated experiments to see whether such effects can be seen in cell cultures. prominently displayed in journal issues (such as being featured on the cover). MIMs however have not yet been widely used by others, presumably because of the effort required to learn the rules of the notation. Our colleagues have found the effort to be well worth while, in part because adhering to the rules of the MIM notation imposes a discipline of logic that enforces clear understanding. In order to help others adopt the notation, we published an article this year that more clearly defines the rules of the notation and that provides a systematic set of examples (Kohn et al. (2006) Mol. Biol. Cell 17: 1-13). The article can serve both as a reference manual and as a tutorial. By publishing this article in a major cell biology journal, we aimed for the broad cell biology community, rather than narrowly for bioinformaticists and systems biologists. The editors selected examples of our molecular interaction maps for display on the journal cover in order to bring our method to the attention of a wide audience of cell biologists. Relevant to bioinformatics and systems biology, we prepared another article (Kohn et al. (2006) Mol. Syst. Biol., in press), in which we address two major issues. First, we show the advantages of the MIM notation relative to other proposed notations. Second, we show how the MIM notation can represent networks for combinatorial simulation, a capability not shared by any other proposed notation. To demonstrate this point, we prepared a concise representation of a combinatorial simulation of the core of epidermal growth factor signaling recently published by Blinov et al, consisting of over 3000 individual reactions. This insight then led to a collaboration with Michael Blinov et al, in which we plan to mesh our MIM notation with their combinatorial network program. On the computation front this year, our group began a simulation study of the role of Mdmx in the response of p53 to DNA damage. In collaboration with Dr. Mirit Aladjem in our Laboratory, we plan to link simulation results with experiments.